{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import re\n",
    "import glob\n",
    "import copy\n",
    "from datetime import datetime\n",
    "\n",
    "with open('../activities_map.json') as f:\n",
    "    activities_map = json.load(f)\n",
    "\n",
    "\n",
    "def is_match_in_activity_list(activity_list, activity):\n",
    "    for activity_name in activity_list:\n",
    "        if activity_name.endswith(activity):\n",
    "            return True\n",
    "\n",
    "    return False\n",
    "\n",
    "def get_activity_coverage(app_name, droidbot_result_path):\n",
    "    states_in_utg = set()\n",
    "    assert os.path.exists(droidbot_result_path), f'{droidbot_result_path} does not exist'\n",
    "    \n",
    "    if os.path.exists(os.path.join(droidbot_result_path, 'utg.js')):\n",
    "        with open(os.path.join(droidbot_result_path, 'utg.js')) as f:\n",
    "            content = f.readlines()\n",
    "        states_in_utg = set()\n",
    "        for line in content:\n",
    "            line = line.strip()\n",
    "            if line.startswith('\"image\":'):\n",
    "                state_tag = line.split('states/screen_')[-1].split('.png')[0]\n",
    "                states_in_utg.add(f'state_{state_tag}.json')\n",
    "\n",
    "    state_files = sorted(glob.glob(os.path.join(droidbot_result_path, 'states', '*.json')))\n",
    "\n",
    "    if len(states_in_utg) > 0:\n",
    "        state_files = [state_file for state_file in state_files if os.path.basename(state_file) in states_in_utg]\n",
    "\n",
    "    covered_activities_so_far = set()\n",
    "    timeline = []\n",
    "    start_time = None\n",
    "    for state_file in state_files:\n",
    "        with open(state_file) as f:\n",
    "            state = json.load(f)\n",
    "        foreground_activity = state['foreground_activity']\n",
    "        if foreground_activity.endswith('}'):\n",
    "            foreground_activity = foreground_activity[:-1]\n",
    "        foreground_activity = foreground_activity.split('/')[-1]\n",
    "        if is_match_in_activity_list(activities_map[app_name], foreground_activity):\n",
    "            covered_activities_so_far.add(foreground_activity)\n",
    "\n",
    "        timestamp = os.path.basename(state_file).removeprefix('state_').removesuffix('.json')\n",
    "        timestamp = datetime.strptime(timestamp, '%Y-%m-%d_%H%M%S')\n",
    "\n",
    "        if start_time is None:\n",
    "            start_time = timestamp\n",
    "\n",
    "        time_offset = (timestamp - start_time).total_seconds()\n",
    "        if time_offset > 7200:\n",
    "            break\n",
    "\n",
    "        timeline.append((timestamp, len(covered_activities_so_far)))\n",
    "\n",
    "    return timeline, len(activities_map[app_name])\n",
    "\n",
    "\n",
    "def get_droidagent_data(droidagent_result_path):\n",
    "    with open(os.path.join(droidagent_result_path, 'exp_data.json')) as f:\n",
    "        data = json.load(f)\n",
    "    return data['visited_activities'], data['app_activities']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phonograph\n",
      "collect\n",
      "commons\n",
      "AnkiDroid\n",
      "ActivityDiary\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>App Name</th>\n",
       "      <th>coverage</th>\n",
       "      <th>covered_num</th>\n",
       "      <th>technique</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>11</td>\n",
       "      <td>DroidAgent</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>11</td>\n",
       "      <td>NoKnowledge</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>9</td>\n",
       "      <td>NoKnowledgeAndCritique</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>6</td>\n",
       "      <td>Actor-only</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Phonograph</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>total</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>collect</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>13</td>\n",
       "      <td>DroidAgent</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>collect</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>9</td>\n",
       "      <td>NoKnowledge</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>collect</td>\n",
       "      <td>0.243243</td>\n",
       "      <td>9</td>\n",
       "      <td>NoKnowledgeAndCritique</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>collect</td>\n",
       "      <td>0.054054</td>\n",
       "      <td>2</td>\n",
       "      <td>Actor-only</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>collect</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>37</td>\n",
       "      <td>total</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>commons</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>14</td>\n",
       "      <td>DroidAgent</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>commons</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>13</td>\n",
       "      <td>NoKnowledge</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>commons</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>13</td>\n",
       "      <td>NoKnowledgeAndCritique</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>commons</td>\n",
       "      <td>0.411765</td>\n",
       "      <td>7</td>\n",
       "      <td>Actor-only</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>commons</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17</td>\n",
       "      <td>total</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>AnkiDroid</td>\n",
       "      <td>0.681818</td>\n",
       "      <td>15</td>\n",
       "      <td>DroidAgent</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AnkiDroid</td>\n",
       "      <td>0.590909</td>\n",
       "      <td>13</td>\n",
       "      <td>NoKnowledge</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AnkiDroid</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>12</td>\n",
       "      <td>NoKnowledgeAndCritique</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AnkiDroid</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>6</td>\n",
       "      <td>Actor-only</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>AnkiDroid</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>22</td>\n",
       "      <td>total</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ActivityDiary</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>DroidAgent</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ActivityDiary</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>9</td>\n",
       "      <td>NoKnowledge</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ActivityDiary</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>9</td>\n",
       "      <td>NoKnowledgeAndCritique</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ActivityDiary</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>6</td>\n",
       "      <td>Actor-only</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ActivityDiary</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>total</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         App Name  coverage  covered_num               technique  total\n",
       "0      Phonograph  0.916667           11              DroidAgent     12\n",
       "1      Phonograph  0.916667           11             NoKnowledge     12\n",
       "2      Phonograph  0.750000            9  NoKnowledgeAndCritique     12\n",
       "3      Phonograph  0.500000            6              Actor-only     12\n",
       "4      Phonograph  1.000000           12                   total     12\n",
       "5         collect  0.351351           13              DroidAgent     37\n",
       "6         collect  0.243243            9             NoKnowledge     37\n",
       "7         collect  0.243243            9  NoKnowledgeAndCritique     37\n",
       "8         collect  0.054054            2              Actor-only     37\n",
       "9         collect  1.000000           37                   total     37\n",
       "10        commons  0.823529           14              DroidAgent     17\n",
       "11        commons  0.764706           13             NoKnowledge     17\n",
       "12        commons  0.764706           13  NoKnowledgeAndCritique     17\n",
       "13        commons  0.411765            7              Actor-only     17\n",
       "14        commons  1.000000           17                   total     17\n",
       "15      AnkiDroid  0.681818           15              DroidAgent     22\n",
       "16      AnkiDroid  0.590909           13             NoKnowledge     22\n",
       "17      AnkiDroid  0.545455           12  NoKnowledgeAndCritique     22\n",
       "18      AnkiDroid  0.272727            6              Actor-only     22\n",
       "19      AnkiDroid  1.000000           22                   total     22\n",
       "20  ActivityDiary  1.000000           10              DroidAgent     10\n",
       "21  ActivityDiary  0.900000            9             NoKnowledge     10\n",
       "22  ActivityDiary  0.900000            9  NoKnowledgeAndCritique     10\n",
       "23  ActivityDiary  0.600000            6              Actor-only     10\n",
       "24  ActivityDiary  1.000000           10                   total     10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows = []\n",
    "\n",
    "for project in os.listdir('../data/'):\n",
    "    if project == \"QuickChat\":\n",
    "        continue\n",
    "    if project == '.keep':\n",
    "        continue\n",
    "\n",
    "    result_path = os.path.join('../data/', project)\n",
    "    timeline_droidagent, num_all_activities = get_activity_coverage(project, result_path)\n",
    "    \n",
    "    # gptdroid = actor-only version of droidagent\n",
    "    gptdroid_result_path = glob.glob(f'../baselines/gptdroid/{project}')\n",
    "    timeline_actoronly, _ = get_activity_coverage(project, gptdroid_result_path[0])\n",
    "\n",
    "    noknowledge_baseline_result_path = glob.glob(f'../ablation/ablation_noknowledge_baseline_noknowledge_output_coverage/{project}')\n",
    "    if len(noknowledge_baseline_result_path) == 0:\n",
    "        continue\n",
    "    noknowledge_baseline_result_path = noknowledge_baseline_result_path[0]\n",
    "    timeline_noknowledge, _ = get_activity_coverage(project, noknowledge_baseline_result_path)\n",
    "    if len(noknowledge_baseline_result_path) == 0:\n",
    "        continue\n",
    "\n",
    "    # infer droidagent_nocritique result path\n",
    "    nocritique_baseline_result_path = glob.glob(f'../ablation/ablation_nocritique_baseline_nocritique_output_coverage/{project}')\n",
    "    if len(nocritique_baseline_result_path) == 0:\n",
    "        continue\n",
    "    nocritique_baseline_result_path = nocritique_baseline_result_path[0]\n",
    "    timeline_nocritique, _ = get_activity_coverage(project, nocritique_baseline_result_path)\n",
    "    \n",
    "    print(project)\n",
    "    rows.append({\n",
    "        'App Name': project,\n",
    "        'coverage': timeline_droidagent[-1][1]/num_all_activities,\n",
    "        'covered_num': timeline_droidagent[-1][1],\n",
    "        'technique': 'DroidAgent',\n",
    "        'total': num_all_activities,\n",
    "    })\n",
    "    rows.append({\n",
    "        'App Name': project,\n",
    "        'coverage': timeline_noknowledge[-1][1]/num_all_activities,\n",
    "        'covered_num': timeline_noknowledge[-1][1],\n",
    "        'technique': 'NoKnowledge',\n",
    "        'total': num_all_activities,\n",
    "    })\n",
    "    rows.append({\n",
    "        'App Name': project,\n",
    "        'coverage': timeline_nocritique[-1][1]/num_all_activities,\n",
    "        'covered_num': timeline_nocritique[-1][1],\n",
    "        'technique': 'NoKnowledgeAndCritique',\n",
    "        'total': num_all_activities,\n",
    "    })\n",
    "    rows.append({\n",
    "        'App Name': project,\n",
    "        'coverage': timeline_actoronly[-1][1]/num_all_activities,\n",
    "        'covered_num': timeline_actoronly[-1][1],\n",
    "        'technique': 'Actor-only',\n",
    "        'total': num_all_activities,\n",
    "    })\n",
    "    rows.append({\n",
    "        'App Name': project,\n",
    "        'coverage': 1,\n",
    "        'covered_num': num_all_activities,\n",
    "        'technique': 'total',\n",
    "        'total': num_all_activities,\n",
    "    })\n",
    "\n",
    "import pandas as pd\n",
    "df = pd.DataFrame(rows)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>technique</th>\n",
       "      <th>Actor-only</th>\n",
       "      <th>DroidAgent</th>\n",
       "      <th>NoKnowledge</th>\n",
       "      <th>NoKnowledgeAndCritique</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>App Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ActivityDiary</th>\n",
       "      <td>6 (0.6)</td>\n",
       "      <td>10 (1.0)</td>\n",
       "      <td>9 (0.9)</td>\n",
       "      <td>9 (0.9)</td>\n",
       "      <td>10 (1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AnkiDroid</th>\n",
       "      <td>6 (0.27)</td>\n",
       "      <td>15 (0.68)</td>\n",
       "      <td>13 (0.59)</td>\n",
       "      <td>12 (0.55)</td>\n",
       "      <td>22 (1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phonograph</th>\n",
       "      <td>6 (0.5)</td>\n",
       "      <td>11 (0.92)</td>\n",
       "      <td>11 (0.92)</td>\n",
       "      <td>9 (0.75)</td>\n",
       "      <td>12 (1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collect</th>\n",
       "      <td>2 (0.05)</td>\n",
       "      <td>13 (0.35)</td>\n",
       "      <td>9 (0.24)</td>\n",
       "      <td>9 (0.24)</td>\n",
       "      <td>37 (1.0)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>commons</th>\n",
       "      <td>7 (0.41)</td>\n",
       "      <td>14 (0.82)</td>\n",
       "      <td>13 (0.76)</td>\n",
       "      <td>13 (0.76)</td>\n",
       "      <td>17 (1.0)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "technique     Actor-only DroidAgent NoKnowledge NoKnowledgeAndCritique  \\\n",
       "App Name                                                                 \n",
       "ActivityDiary    6 (0.6)   10 (1.0)     9 (0.9)                9 (0.9)   \n",
       "AnkiDroid       6 (0.27)  15 (0.68)   13 (0.59)              12 (0.55)   \n",
       "Phonograph       6 (0.5)  11 (0.92)   11 (0.92)               9 (0.75)   \n",
       "collect         2 (0.05)  13 (0.35)    9 (0.24)               9 (0.24)   \n",
       "commons         7 (0.41)  14 (0.82)   13 (0.76)              13 (0.76)   \n",
       "\n",
       "technique         total  \n",
       "App Name                 \n",
       "ActivityDiary  10 (1.0)  \n",
       "AnkiDroid      22 (1.0)  \n",
       "Phonograph     12 (1.0)  \n",
       "collect        37 (1.0)  \n",
       "commons        17 (1.0)  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['covered'] = df.apply(lambda row: f'{row.covered_num} ({round(row.coverage, 2)})', axis=1)\n",
    "df.pivot(index='App Name', columns='technique', values='covered').round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n4/5pbkhgg90kn29trx7cx_t10c0000gn/T/ipykernel_65683/821051348.py:12: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.barplot(x=\"technique\", y=\"coverage\", data=df, palette='Set2', errorbar='sd')\n",
      "/var/folders/n4/5pbkhgg90kn29trx7cx_t10c0000gn/T/ipykernel_65683/821051348.py:14: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=60, fontsize=12)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'DroidAgent'),\n",
       " Text(1, 0, 'NoKnowledge'),\n",
       " Text(2, 0, 'NoKnowledgeAndCritique'),\n",
       " Text(3, 0, 'Actor-only')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# aggregated\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "df = df[df['technique'] != 'total']\n",
    "\n",
    "plt.figure(figsize=(5, 3))\n",
    "plt.title('Activity Coverage Comparison (Ablation)')\n",
    "# font size \n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('whitegrid')\n",
    "ax = sns.barplot(x=\"technique\", y=\"coverage\", data=df, palette='Set2', errorbar='sd')\n",
    "ax.set(xlabel='Technique', ylabel='Coverage')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=60, fontsize=12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n4/5pbkhgg90kn29trx7cx_t10c0000gn/T/ipykernel_65683/3546021090.py:16: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), fontsize=13)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# subfigure for each application\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "df = df[df['technique'] != 'total']\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.title('Activity Coverage Comparison (Ablation)')\n",
    "# font size\n",
    "sns.set(font_scale=1.3)\n",
    "sns.set_style('whitegrid')\n",
    "ax = sns.barplot(x=\"App Name\", y=\"coverage\", hue=\"technique\", data=df, palette='Set2')\n",
    "ax.set(xlabel='', ylabel='Activity Coverage')\n",
    "ax.set_ylabel('Activity Coverage', fontsize=13)\n",
    "ax.set_xticklabels(ax.get_xticklabels(), fontsize=13)\n",
    "# legend outside\n",
    "ax.legend(bbox_to_anchor=(1.02, 1), loc=2, borderaxespad=0., fontsize=11)\n",
    "plt.tight_layout()\n",
    "plt.savefig('./RQ3_ablation.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n4/5pbkhgg90kn29trx7cx_t10c0000gn/T/ipykernel_65683/2786765967.py:12: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.barplot(x=\"technique\", y=\"coverage\", data=df, palette='Set2', errorbar='sd')\n",
      "/var/folders/n4/5pbkhgg90kn29trx7cx_t10c0000gn/T/ipykernel_65683/2786765967.py:14: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_xticklabels(ax.get_xticklabels(), rotation=80)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# aggregated\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "df = df[df['technique'] != 'total']\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "plt.title('Activity Coverage Comparison (Ablation)')\n",
    "# font size \n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('whitegrid')\n",
    "ax = sns.barplot(x=\"technique\", y=\"coverage\", data=df, palette='Set2', errorbar='sd')\n",
    "ax.set(xlabel='Technique', ylabel='Coverage')\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=80)\n",
    "plt.tight_layout()\n",
    "plt.savefig('./RQ3_ablation_agg.pdf', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "testing-agent",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
